{
  "id": 19030,
  "url": "https://arxiv.org/abs/2608.12198v1",
  "title": "Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment",
  "summary": "Recent research in machine and deep learning has shown the potential of learningbased motion planning approaches to improve the driving behavior of automated vehicles, especially in complex environments. However, their complex nature and lack of transparency can hinder explainability and trustworthiness and complicate safety assurance. Motivated by these challenges, we propose a hybrid planning architecture that combines the advantages of machine learning with the verifiability and the determini",
  "authors": "Jean-Pierre Busch, Guido Linden, Jan Bergmann, Lutz Eckstein",
  "category": "research",
  "topics": "transparency,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T15:52:18.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/19030",
  "original_url": "https://arxiv.org/abs/2608.12198v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}